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- W2101681305 abstract "This paper addresses the problem of efficient execution of a batch of data-intensive tasks with batch-shared I/O behavior, on coupled storage and compute clusters. Two scheduling schemes are proposed: 1) a 0-1 integer programming (IP) based approach, which couples task scheduling and data replication, and 2) a bi-level hypergraph partitioning based heuristic approach (BiPartition), which decouples task scheduling and data replication. The experimental results show that: 1) the IP scheme achieves the best batch execution time, but has significant scheduling overhead, thereby restricting its application to small scale workloads, and 2) the BiPartition scheme is a better fit for larger workloads and systems - it has very low scheduling overhead and no more than 5-10% degradation in solution quality, when compared with the IP based approach" @default.
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- W2101681305 date "2006-07-21" @default.
- W2101681305 modified "2023-09-24" @default.
- W2101681305 title "Task Scheduling and File Replication for Data-Intensive Jobs with Batch-shared I/O" @default.
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- W2101681305 doi "https://doi.org/10.1109/hpdc.2006.1652155" @default.
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